Periconceptional Serum Creatinine and Risk of Childhood Autism Spectrum Disorder: A Research Letter
Bibliographic record
Abstract
Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition that manifests in early childhood, in which the maternal metabolic syndrome may be a risk factor. The kidney is a barometer of maternal metabolic syndrome duration and severity. Objective: The main objective of this study is to determine whether periconceptional kidney function is associated with ASD in early childhood. Design Setting and Participants: This retrospective population-based cohort study was completed in Ontario, Canada. Included were singleton children born in an Ontario hospital between April 2007 and March 2021, who were alive at age 48 months and whose mother had a recorded prepregnancy body mass index (BMI) and a measured serum creatinine (SCr) between 120 days preconception and 28 days postconception. Measurement: The main study outcome was a diagnosis of ASD between ages 24 and 48 months. Methods: Relative risks (RRs) of ASD in association with periconceptional SCr were generated using modified Poisson regression and adjusted for several confounders. Results: The cohort comprised 86 054 women, who had 89 677 liveborn children surviving to at least 48 months of age. There was no significant association between periconceptional SCr and ASD (RR: 0.86; 95 % confidence interval: [0.67, 1.10]). Limitations: Selection bias may have arisen had SCr been ordered on clinical grounds. Conclusions: Further study is warranted to determine whether prepregnancy glomerular hyperfiltration is a marker of ASD and other behavioral conditions in childhood. To do so, a more accurate measure of hyperfiltration is needed than SCr.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".